Generally speaking, vehicle re-identification (“vehicle re-ID”) is a technology that aims to identify a vehicle of interest across images taken by multiple cameras. For example, after a vehicle has been captured by a first camera and has exited the field-of-view (FOV) of the first camera, vehicle re-identification technologies attempt to re-identify the vehicle when it enters the FOV of other cameras. This technology is useful in a variety of different contexts, such as surveillance systems and intelligent transportation systems.
Various technical obstacles impede the implementation of an effective and accurate vehicle re-identification solution. One technical challenge relates to tracking a vehicle across multiple cameras when the views of the multiple cameras do not overlap with one another and/or when the vehicle is captured from different viewpoints. When the views of the cameras do not overlap, the camera system cannot directly track the path of the vehicle and must be able to re-identify the vehicle when it reappears in the FOV of other cameras. However, a vehicle captured from different viewpoints usually has a dramatically different visual appearance in each of the viewpoints. For example, consider the scenario in which a first camera captures an image of the vehicle from a rear viewpoint and a second camera captures another image of the vehicle from a side viewpoint. The task of matching the vehicles in the images is difficult because the visual appearance of the vehicle varies greatly between the two images. Another technical difficulty associated with vehicle re-identification relates to distinguishing between similar vehicles that are captured from the same viewpoint. For example, consider another scenario in which two different vehicles of the same color and model are captured in images taken from the same viewpoint. In this case, the task of matching the vehicles in the images is difficult given the similar visual appearances of the vehicle in the images.
Many conventional vehicle re-identification methods rely on license plate recognition (LPR) techniques or spatial-temporal tracking methods (e.g., which utilize timing information to try to track and identify vehicles) to address the vehicle re-identification task. However, these conventional methods are not practical in many real-world situations. For example, LPR-based re-identification methods typically require images to be taken from specific viewpoints (i.e., either the front or rear viewpoint) and the images must be captured using high-resolution cameras. These obstacles prevent vehicle re-identification technologies from being integrated into existing systems which do not have high-resolution equipment, or which do not have cameras situated at the appropriate viewpoints. With respect to spatial-temporal tracking methods, many camera systems are not configured to generate spatial-temporal information or to process it in any meaningful way. Retrofitting an existing camera system to track and process spatial-temporal information can require extensive upgrades to the camera equipment and/or software running on the back-end of the system.